remote sensing, hyperspectral, chlorophyll a, turbidity, eutrophication
Remote sensing data were successfully used to estimate spatial and temporal variation of optical water quality parameters such as chlorophyll a, turbidity and Total Suspended Solids (TSS) of the Great Miami River, Ohio. During the summer of 1999, spectral data were collected with a hand-held spectroradiometer, a laboratory spectrometer and airborne hyperspectral sensors. Approximately 80 km of the Great Miami River were imaged during a flyover with a Compact Airborne Spectrographic Imager (CASI) sensor. Approximately 10 km were imaged during a second flyover to repeat coverage of the urban and industrial influences around the city of Dayton, Ohio. Instream measurements of water quality data such as turbidity levels, chlorophyll a concentrations, and Secchi-disk depth were acquired on the same days as the flyovers. Relationships between optical water quality parameters and one or two broad wavebands were determined. An attempt was made to utilize portions of the electromagnetic spectrum, which minimize the effects of atmospheric anomalies, in turn normalizing the spectrally additive constants in all wavebands. Because this assumption was not met for turbidity, a first derivative approach was used. The derivative reflectance is an alternative and theoretically more robust relationship between the water quality parameter and adjacent wavebands. The ratio of wavebands 705 and 672 were highly correlated with chlorophyll a (R2 = 0.74) and the first derivative of wavebands 700 and 675 were highly correlated with turbidity (R2 = 0.79). These correlations made it possible to estimate the concentration of chlorophyll a and level of turbidity in portions of the Great Miami River where only hyperspectral data were taken. Maps of the relative distributions of chlorophyll a and turbidity were created from the hyperspectral images of the river.
BYU ScholarsArchive Citation
"The Selection of Narrow Wavebands for Optimizing Water Quality Monitoring on the Great Miami River, Ohio using Hyperspectral Remote Sensor Data,"
Journal of Spatial Hydrology: Vol. 1
, Article 3.
Available at: https://scholarsarchive.byu.edu/josh/vol1/iss1/3